%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import seaborn as sns
import scanpy as sc
from glob import iglob
import scrublet as scr
import anndata
import scanpy.external as sce
import scvelo as scv
#import scrublet as scr # requires 'pip install scrublet'
import os
import sklearn
import bbknn
from sklearn.linear_model import LogisticRegression
import matplotlib as mpl
import scipy
import matplotlib.pyplot as plt
import pickle
np.random.seed(0)
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_versions()
sc.settings.set_figure_params(dpi=100,dpi_save=800) # low dpi (dots per inch) yields small inline figures
----- anndata 0.8.0 scanpy 1.9.1 ----- OpenSSL 21.0.0 PIL 9.0.1 aa8f2297d25b4dc6fd3d98411eb3ba53823c4f42 NA absl NA annoy NA asciitree NA asttokens NA astunparse 1.6.3 backcall 0.2.0 bbknn NA bcrypt 3.2.0 beta_ufunc NA binom_ufunc NA boto3 1.21.32 botocore 1.24.32 bottleneck 1.3.4 brotli NA cairo 1.21.0 certifi 2021.10.08 cffi 1.15.0 chardet 4.0.0 charset_normalizer 2.0.4 cloudpickle 2.0.0 colorama 0.4.4 cryptography 3.4.8 cycler 0.10.0 cython_runtime NA cytoolz 0.11.0 dask 2022.02.1 dateutil 2.8.2 debugpy 1.5.1 decorator 5.1.1 defusedxml 0.7.1 dot_parser NA entrypoints 0.4 executing 0.8.3 fasteners 0.17.3 flatbuffers NA fsspec 2022.02.0 gast NA google NA h5py 3.6.0 idna 3.3 igraph 0.9.11 ipykernel 6.9.1 ipython_genutils 0.2.0 ipywidgets 7.6.5 jedi 0.17.2 jinja2 3.1.2 jmespath 0.10.0 joblib 1.1.0 jupyter_server 1.13.5 keras 2.9.0 kiwisolver 1.3.2 leidenalg 0.8.10 llvmlite 0.38.0 louvain 0.7.1 markupsafe 2.0.1 matplotlib 3.5.1 matplotlib_inline NA mkl 2.4.0 mpl_toolkits NA msgpack 1.0.2 natsort 8.1.0 nbinom_ufunc NA nt NA ntsecuritycon NA numba 0.55.1 numcodecs 0.10.0 numexpr 2.8.1 numpy 1.21.5 opt_einsum v3.3.0 packaging 21.3 pandas 1.4.2 parso 0.7.1 pickleshare 0.7.5 pkg_resources NA prompt_toolkit 3.0.20 psutil 5.8.0 pure_eval 0.2.2 pycparser 2.21 pydev_ipython NA pydevconsole NA pydevd 2.6.0 pydevd_concurrency_analyser NA pydevd_file_utils NA pydevd_plugins NA pydevd_tracing NA pydot 1.4.2 pygments 2.11.2 pynndescent 0.5.7 pyparsing 3.0.4 pythoncom NA pytz 2021.3 pywintypes NA requests 2.27.1 scipy 1.7.3 scrublet NA scvelo 0.2.4 seaborn 0.11.2 session_info 1.0.0 setuptools 61.2.0 setuptools_scm NA six 1.16.0 sklearn 1.0.2 snappy NA socks 1.7.1 sphinxcontrib NA stack_data 0.2.0 statsmodels 0.13.2 tblib 1.7.0 tensorboard 2.9.1 tensorflow 2.9.1 termcolor 1.1.0 texttable 1.6.4 threadpoolctl 2.2.0 tlz 0.11.0 toolz 0.11.2 tornado 6.1 tqdm 4.64.0 traitlets 5.1.1 typing_extensions NA umap 0.5.3 urllib3 1.26.9 wcwidth 0.2.5 win32api NA win32com NA win32security NA wrapt 1.12.1 yaml 6.0 zarr 2.12.0 zipp NA zmq 22.3.0 zope NA ----- IPython 8.2.0 jupyter_client 6.1.12 jupyter_core 4.9.2 jupyterlab 3.3.2 notebook 6.4.8 ----- Python 3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)] Windows-10-10.0.19044-SP0 ----- Session information updated at 2022-09-07 09:01
adata = sc.read_h5ad('./01.single data/scRNA_qc.h5ad')
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
normalizing counts per cell
finished (0:00:00)
sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
print("Highly variable genes: %d"%sum(adata.var.highly_variable))
sc.pl.highly_variable_genes(adata)
extracting highly variable genes
finished (0:00:01)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
Highly variable genes: 2987
sc.pp.regress_out(adata, ['n_counts', 'pct_counts_mt'])
sc.pp.scale(adata, max_value=10)
regressing out ['n_counts', 'pct_counts_mt']
sparse input is densified and may lead to high memory use
finished (0:22:29)
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_variance_ratio(adata, log=True,n_pcs = 50)
sns.lineplot(data = adata.uns['pca']['variance_ratio'],palette= 'tab10',linewidth = 3)
adata.uns['pca']['variance']
sns.lineplot(data =adata.uns['pca']['variance'],palette='tab10',linewidth = 3)
computing PCA
on highly variable genes
with n_comps=50
finished (0:00:11)
<AxesSubplot:>
%%time
# trying out harmony here
# Check PCA again - needed for harmony - and extract PCA matrix and batch array
n_pcs = 30
pca = adata.obsm['X_pca'][:, 0:n_pcs]
batch = adata.obs['ID']
CPU times: total: 0 ns Wall time: 0 ns
# Batch-correct the PCA using HARMONY method
%load_ext rpy2.ipython
%%time
%%R -i pca -i batch -o hem
library(harmony)
library(magrittr)
hem <- HarmonyMatrix(pca, batch, theta=1, do_pca=FALSE)
hem = data.frame(hem)
Exception ignored from cffi callback <function _consolewrite_ex at 0x000001FB01BE33A0>:
Traceback (most recent call last):
File "D:\conda\lib\site-packages\rpy2\rinterface_lib\callbacks.py", line 133, in _consolewrite_ex
s = conversion._cchar_to_str_with_maxlen(buf, n, _CCHAR_ENCODING)
File "D:\conda\lib\site-packages\rpy2\rinterface_lib\conversion.py", line 138, in _cchar_to_str_with_maxlen
s = ffi.string(c, maxlen).decode(encoding)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd4 in position 0: invalid continuation byte
R[write to console]: Harmony 1/10
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R[write to console]: Harmony 2/10
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R[write to console]: Harmony 3/10
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R[write to console]: Harmony 4/10
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R[write to console]: Harmony 5/10
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R[write to console]: Harmony 6/10
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R[write to console]: Harmony 7/10
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R[write to console]: Harmony converged after 7 iterations
CPU times: total: 4min 51s Wall time: 4min 51s
# Add harmony values to the anndata object
adata.obsm['X_pca'] = hem.values
for i in range(10,60,5):
sc.pp.neighbors(adata,n_pcs=30, n_neighbors= i)
sc.tl.umap(adata)
print(f'neighbors的数值为：{i}')
sc.pl.umap(adata)
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:43)
neighbors的数值为：10
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:03)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:43)
neighbors的数值为：15
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:03)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:47)
neighbors的数值为：20
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:04)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:51)
neighbors的数值为：25
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:05)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:55)
neighbors的数值为：30
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:06)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:57)
neighbors的数值为：35
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:07)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:01:00)
neighbors的数值为：40
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:09)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:01:02)
neighbors的数值为：45
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:11)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:01:04)
neighbors的数值为：50
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:14)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:01:07)
neighbors的数值为：55
sc.pp.neighbors(adata,n_pcs=30, n_neighbors= 55)
sc.tl.umap(adata)
sc.pl.umap(adata)
computing neighbors
using 'X_pca' with n_pcs = 30
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:14)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:01:06)
adata1 = sc.read_h5ad('./01.single data/scRNA_celltype.h5ad')
umap = adata1.obsm['X_umap']
adata.obsm['X_umap'] = umap
sc.pl.umap(adata)
for i in range(1,16):
sc.tl.leiden(adata,resolution=round(i*0.1,1),key_added=f'cluster_{round(i*0.1,1)}')
running Leiden clustering
finished: found 13 clusters and added
'cluster_0.1', the cluster labels (adata.obs, categorical) (0:02:05)
running Leiden clustering
finished: found 17 clusters and added
'cluster_0.2', the cluster labels (adata.obs, categorical) (0:01:45)
running Leiden clustering
finished: found 21 clusters and added
'cluster_0.3', the cluster labels (adata.obs, categorical) (0:04:08)
running Leiden clustering
finished: found 22 clusters and added
'cluster_0.4', the cluster labels (adata.obs, categorical) (0:03:00)
running Leiden clustering
finished: found 22 clusters and added
'cluster_0.5', the cluster labels (adata.obs, categorical) (0:03:35)
running Leiden clustering
finished: found 24 clusters and added
'cluster_0.6', the cluster labels (adata.obs, categorical) (0:05:13)
running Leiden clustering
finished: found 25 clusters and added
'cluster_0.7', the cluster labels (adata.obs, categorical) (0:03:48)
running Leiden clustering
finished: found 25 clusters and added
'cluster_0.8', the cluster labels (adata.obs, categorical) (0:04:42)
running Leiden clustering
finished: found 26 clusters and added
'cluster_0.9', the cluster labels (adata.obs, categorical) (0:03:26)
running Leiden clustering
finished: found 28 clusters and added
'cluster_1.0', the cluster labels (adata.obs, categorical) (0:02:34)
running Leiden clustering
finished: found 30 clusters and added
'cluster_1.1', the cluster labels (adata.obs, categorical) (0:03:19)
running Leiden clustering
finished: found 31 clusters and added
'cluster_1.2', the cluster labels (adata.obs, categorical) (0:04:36)
running Leiden clustering
finished: found 33 clusters and added
'cluster_1.3', the cluster labels (adata.obs, categorical) (0:05:04)
running Leiden clustering
finished: found 33 clusters and added
'cluster_1.4', the cluster labels (adata.obs, categorical) (0:08:35)
running Leiden clustering
finished: found 36 clusters and added
'cluster_1.5', the cluster labels (adata.obs, categorical) (0:04:41)
sc.pl.umap(adata,color = [f'cluster_{round(i*0.1,1)}' for i in range(1,16)],ncols = 3,wspace=0.5)
sc.pl.umap(adata, color=['Type','cluster_0.6'],add_outline=True, legend_loc='right margin',size=15,
legend_fontsize=15, legend_fontoutline= 1,frameon= True,wspace=0.3)
adata.obs['Type'].value_counts()
Control 45618 Vehicle 16099 Drug 13528 Taxol 12831 Name: Type, dtype: int64
adata.obs['Type'].value_counts()
Control 45618 Vehicle 16099 Drug 13528 Taxol 12831 Name: Type, dtype: int64
sc.tl.rank_genes_groups(adata,'cluster_0.6',method='wilcoxon')
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
ranking genes
finished: added to `.uns['rank_genes_groups']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:12:11)
pd.DataFrame(adata.uns['rank_genes_groups']['names']).head(35)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Apoe | Cst3 | Gramd3 | Plp1 | Cd74 | S100a9 | Ctla4 | Srgn | Dusp9 | Plcb1 | ... | Krt71 | Emb | Col1a2 | Plp1 | Syne1 | Lsamp | Siglech | Hbb-bs | Sptbn1 | Itgb8 |
| 1 | Ctsd | Ctss | Ptpn22 | Mbp | H2-Aa | S100a8 | Icos | Clec4d | Gm37035 | S100a4 | ... | Gm47501 | Rora | Sparc | Apod | Fas | Opcml | Bst2 | Hba-a1 | Flt1 | Timp3 |
| 2 | C1qa | Basp1 | Itk | Pcdh9 | H2-Ab1 | Lcn2 | Rora | A630073D07Rik | Gm44780 | Gda | ... | Gm10610 | Ikzf3 | Col1a1 | Mbp | Dbi | Dscam | St8sia4 | Hbb-bt | Pecam1 | Utrn |
| 3 | C1qb | C1qb | Cblb | Nkain2 | H2-Eb1 | Ngp | Ets1 | Vmn2r117 | Gm13855 | Ifitm3 | ... | Gm48923 | Icos | Dcn | Gm42418 | Klf5 | Lhfpl3 | Tcf4 | Hba-a2 | Epas1 | Stard13 |
| 4 | Trem2 | Zeb2 | Vps37b | Mal | Lyz2 | Wfdc21 | Tnfrsf4 | Gm2244 | Gm15792 | F13a1 | ... | Gm4858 | Zbtb16 | Gsn | Pcdh9 | Cd24a | Nrxn1 | Aff3 | Gypa | Ptprb | Dmd |
| 5 | C1qc | Fnip2 | Ets1 | Aplp1 | Lgals3 | Pglyrp1 | Tnfrsf9 | Nat1 | Vmn2r4 | Plac8 | ... | 4930527G23Rik | Camk4 | Mgp | Nkain2 | Gm47708 | Vcan | Ighm | Ermap | Adgrl4 | Nkain2 |
| 6 | Ctss | Rbm47 | Grap2 | Trf | Tgfbi | Ifitm6 | Odc1 | 4930533N22Rik | Gm42697 | Emilin2 | ... | Gm40638 | Il7r | Bgn | Ptgds | Gm15082 | Tnr | Tspan13 | Hmbs | Igfbp7 | Sparc |
| 7 | Hexb | Atf3 | Mbnl1 | Cnp | Cybb | Syne1 | Kdm2b | Ighv3-6 | Olfr1152 | Lyz2 | ... | Cpn2 | Dgat1 | Cfh | Camk1d | Klhl1 | Nlgn1 | Plac8 | Mki67 | Nfib | Mpz |
| 8 | Cst3 | Csf1r | Trbc2 | Scd2 | Psap | Hdc | Il2rb | Gm29674 | 9230009I02Rik | Ms4a6c | ... | Ang2 | St6galnac3 | Cped1 | Cldn11 | Gm48821 | Lrrc4c | Mef2c | Ank1 | Abcg2 | Lmna |
| 9 | Cd14 | Lrmda | Camk4 | Cldn11 | Sdc4 | Camp | Camk4 | 5033428I22Rik | Dkk1 | Thbs1 | ... | Gm26654 | Cxcr6 | Serpinh1 | Car2 | Vmn1r38 | Cadm2 | Rell1 | Slc4a1 | Mecom | Dag1 |
| 10 | Ctsz | Lgmn | Zc3hav1 | Magi2 | Mafb | Anxa1 | Itk | AL845275.1 | Vmn2r83 | Tgfbi | ... | Igkv3-5 | Zeb1 | Igfbp7 | Scd2 | Gm36525 | Pcdh9 | Lrp8 | Car2 | Ptprg | Hspa12a |
| 11 | Ctsb | C1qc | Skap1 | Car2 | Cstb | G0s2 | Hif1a | Gm11636 | Gm11924 | Alox5ap | ... | Gm14635 | Tex2 | Col3a1 | Mobp | Gm48155 | Pcdh7 | Ccr9 | Tfrc | Utrn | Sorcs1 |
| 12 | Timp2 | Epb41l2 | Prkca | Slc24a2 | Ctsc | Sgms2 | Crem | Gm35281 | Lypd8 | Vim | ... | Gm20268 | Crem | Id3 | Mal | Gm34350 | Adgrl3 | Eepd1 | Prdx2 | Bsg | Plekha4 |
| 13 | Spp1 | C1qa | Gimap6 | Mag | Pid1 | Retnlg | Inpp4b | Gm47486 | 4930470H14Rik | Ly6c2 | ... | Ddi1 | Igf1r | Serpinf1 | Aplp1 | Wfdc15a | Sox6 | Mctp2 | Kel | Cdh5 | Plp1 |
| 14 | Cd63 | Hexb | Gm2682 | Apod | Ms4a6c | Cd177 | Tnfrsf18 | Gm15856 | Slc51b | Fn1 | ... | Igkv14-111 | D16Ertd472e | Nedd4 | Trf | Gm44204 | Cntn1 | Ly6d | Slc25a21 | Egfl7 | Ahnak |
| 15 | Tyrobp | Ctsb | Pde7a | Qk | Cxcl16 | Msra | Akap13 | Slc12a1 | Gm31138 | Lgals3 | ... | Ighv8-10 | Itk | Fstl1 | Tmeff2 | 2610318M16Rik | Ptprz1 | Gpr171 | Sox6 | Eng | Qk |
| 16 | Abca1 | Ctsd | Ifngr1 | Mobp | Fcgr2b | S100a11 | Trac | Trim80 | 4930402F11Rik | Vav3 | ... | Npbwr1 | Nr3c1 | Itm2a | Magi2 | Trav13d-1 | Grid2 | Lgals1 | Cd24a | Tcf4 | Afap1l2 |
| 17 | Cd68 | Tanc2 | Prkch | Edil3 | Pla2g7 | Ltf | S100a10 | Gm49497 | Gm31786 | Ccl9 | ... | Fam162b | Vps37b | Zbtb20 | S100a9 | Gm50114 | Adgrb3 | Tex2 | Hemgn | Cldn5 | Sorbs1 |
| 18 | Ctsl | Tns3 | Kdm2b | Cryab | Anxa5 | Rasa2 | Cd2 | Eras | Gm13206 | F10 | ... | Gm16144 | Il18r1 | Cald1 | Slc24a2 | 4930556G01Rik | Plcb1 | Sell | Slc25a37 | Erg | Megf9 |
| 19 | Fcrls | Mafb | Gimap4 | Sept4 | Mmp14 | Hp | Ndfip1 | Fam43b | Gm29825 | Arhgap26 | ... | 1700001G01Rik | Il2rb | Gpc6 | Mag | 4930458B22Rik | Erbb4 | Blnk | Fech | Itm2a | Hspg2 |
| 20 | Syngr1 | Pmp22 | Zeb1 | Dbndd2 | Ftl1 | Plaur | Rabgap1l | Gm40038 | Lipo4 | S100a6 | ... | Gm26650 | Nrip1 | Col5a2 | Cnp | Adam28 | Pcdh15 | Cd7 | Ctse | Ly6c1 | Lama2 |
| 21 | Ftl1 | C3ar1 | Ms4a4b | Tmeff2 | Npc2 | Adpgk | Cd3g | Gm38661 | Gm11642 | Ccr2 | ... | Gm49710 | Kdm2b | Igfbp6 | AY036118 | Gm17266 | Ppfibp1 | Selplg | Cpox | Ebf1 | Tead1 |
| 22 | Csf1r | Apoe | Emb | Plcl1 | Vim | Arhgdib | Cd3d | Gm16143 | AB041806 | Ifitm2 | ... | Kcna10 | Ramp3 | Mmp14 | Edil3 | A930002I21Rik | Mdga2 | Irf8 | Spta1 | Tjp1 | Ank3 |
| 23 | Dock4 | Lhfpl2 | Ablim1 | Ermn | Sdcbp | Mcemp1 | Itpr1 | Gm5458 | Gm15245 | Napsa | ... | Gm28410 | Ets1 | Serping1 | Apoc1 | Gm49124 | Nav3 | Ly6e | Sptb | Ppfibp1 | Sptbn1 |
| 24 | Mpeg1 | Cadm1 | Cd3d | Lrp1b | Prdx1 | Gsr | Dgat1 | Gm11264 | Ighv9-3 | Ahnak | ... | Gm49932 | Il23r | Bicc1 | Ptprd | Gm10646 | Lrp1b | Cytip | Rhd | Wwtr1 | Auts2 |
| 25 | Cd83 | Trem2 | Cd28 | Ptprd | Ninj1 | Sp140 | Aebp2 | Dcaf12l2 | Gm14435 | Cd44 | ... | Krtap28-13 | Aebp2 | Egr1 | Qrfprl | Gm45238 | Npas3 | Ncf1 | Aqp1 | Arhgap29 | Art3 |
| 26 | Fth1 | Hpgds | Tmsb10 | Enpp2 | Ctss | Itgam | Trbc2 | Chrdl2 | Defb43 | Cybb | ... | Gm35147 | Tmem64 | Ahnak | Gm17206 | A330094K24Rik | Anks1b | Glcci1 | Alas2 | Tshz2 | Dst |
| 27 | Grn | Ptprm | Fryl | Kif1b | Fth1 | Antxr2 | H2-K1 | Gm14664 | Gm15179 | S100a10 | ... | Avpr1b | Cd164 | Cebpd | Gm13209 | Gkn3 | Il1rapl1 | Flt3 | Gpx1 | Cyyr1 | Kcna1 |
| 28 | Lgmn | Tgfbr1 | Tut4 | Gpm6b | Ctsb | Dgat1 | Tmsb10 | Zfp92 | Spz1 | Esd | ... | Gm35974 | Arap2 | Pcolce | Gm36107 | Traj27 | Csmd1 | Sub1 | Rhag | Adgrf5 | Tenm3 |
| 29 | Fcer1g | Blnk | H2-Q7 | Mast4 | H2-DMa | Igf1r | Cd28 | Trpv1 | Ccnb3 | Actg1 | ... | Gm49156 | Fryl | Klf9 | 9530036O11Rik | 1700010B13Rik | Rtn1 | Cox6a2 | Abcb10 | Itga6 | Pmp22 |
| 30 | Ctsa | Rab7b | Dock10 | Syt11 | Cxcl2 | Alox5ap | Cytip | Akr1cl | Kbtbd13 | Vcan | ... | Gm36231 | Ccr2 | Sptbn1 | Glra3 | Gm15668 | Gria4 | Snx9 | Metap2 | Zbtb20 | Dlc1 |
| 31 | Bcl2a1b | Ccl4 | Gimap3 | Pde4b | Ahnak | Lmnb1 | Cd4 | Gm33125 | Gm15891 | Gsr | ... | Foxe1 | Ppp1cc | Fgfr1 | Uncx | Ube2d2b | Ncam2 | Rabgap1l | E2f2 | Timp3 | Lamb1 |
| 32 | Lyz2 | Cd83 | Stat4 | St18 | Ms4a6d | Tbc1d8 | Prkca | Gm14565 | Cyp2c66 | Msrb1 | ... | Gm30409 | Ifngr1 | Pbx1 | Gm11682 | Nkx2-4 | Brinp3 | Ifnar2 | Snca | Fry | Vcl |
| 33 | Itgb5 | Ly86 | Stk17b | Tspan2 | Cd14 | Prdx5 | S100a4 | 4930568D16Rik | Gm48306 | Trps1 | ... | Gm39329 | Ern1 | Col6a1 | 9530026F06Rik | Trav9-1 | Nxph1 | Sla2 | Tspan33 | Cd200 | Gpm6b |
| 34 | Bcl2a1a | Snx24 | Rundc3b | Sec11c | Fnip2 | Mxd1 | D16Ertd472e | Gm48703 | Gm16180 | Clec4d | ... | Gm28867 | Prelid2 | Rcn3 | Gm6588 | Olfr729 | Luzp2 | Snx5 | Nxpe2 | Ly6a | Serinc5 |
35 rows × 24 columns
result = adata.uns['rank_genes_groups']
groups = result['names'].dtype.names
res = pd.DataFrame( {group + '_' + key: result[key][group] for group in groups for key in ['names', 'pvals','logfoldchanges','pvals_adj','scores']})
res.to_csv("diff_cluster.csv")
sc.pl.umap(adata, color=['cluster_0.6'],add_outline=True, legend_loc='on data',size=18,title='',
legend_fontsize=10, legend_fontoutline= 1,frameon= True)
markers = ["Apoe","Ctss",#Microglial
"Cd3d",#Lymphocyte
"Rora",#Th17
"Nkg7",#NK
"Cd79a","Cd79b",#B
"Lyz2","Cd68",#Macrophage
"S100a8","S100a9",#Neutrophil
'Ccr7',"Cd74",#Dendritic cell
"Ly6c2","Csf1r",#Monocyte
"Aqp4","Gfap",#Astrocyte
"Dcn","Lum",#Fibroblast
"Map2","Dcx",#Neuron
"Tmem212","Foxj1",#Ependymal cell
"Plp1","Opalin",#Oligodendrocyte
"Olig2",#Oligodendrocyte precursor cell少突胶质前体细胞
'Cldn5',"Pecam1",#endothelial cell
"Dcn","Lum",#Fibroblast
"Cdk1","Mki67",#Dividing cell
]
sc.pl.dotplot(adata,markers,'cluster_0.6',cmap='OrRd',swap_axes=True)
sc.settings.set_figure_params(dpi=100,dpi_save=600,figsize=(5,5))
sc.pl.umap(adata, color=['cluster_0.6','Type'],add_outline=True, legend_loc='on data',size=18,
legend_fontsize=10, legend_fontoutline= 1,frameon= True,wspace=0.3)
sc.pl.umap(adata,color=["S100a9","Gfap","C1qc","Cd14"],ncols=4,cmap='OrRd',size=15,legend_fontsize=12)
def annot_prelim(cluster):
if cluster in ["0","2","8"]:
return('Microglial')
elif cluster in ['1','7','14']:
return('Lymphocyte')
elif cluster in ['3']:
return('OPC')
elif cluster in ['4','9']:
return('Macrophages')
elif cluster in ['5','6','15']:
return('Neutrophil')
elif cluster =='10':
return('Dendritic cell')
elif cluster =='11':
return('NK cell')
elif cluster =='12':
return('B cell')
elif cluster =='13':
return('Monocyte')
elif cluster =='16':
return('Fibroblast')
elif cluster =='17':
return('Astrocyte')
elif cluster =='18':
return('ODC')
elif cluster =='19':
return('Ependymal')
elif cluster =='20':
return('Neuron')
elif cluster =='21':
return('Dividing cell')
elif cluster =='22':
return('Endothelial')
else:
return('Th17')
adata.obs['Celltype'] = adata.obs['cluster_0.6'].map(annot_prelim)
sc.settings.set_figure_params(dpi=100,dpi_save=600,figsize=(6,6))
sc.pl.umap(adata, color='Celltype',add_outline=True, legend_loc='right margin',
legend_fontsize=15, legend_fontoutline= 1,frameon= True)
adata.obs["Celltype"] = adata.obs.Celltype.cat.set_categories([
'Microglial','Astrocyte', 'OPC','ODC',
'Neuron','Ependymal','Fibroblast','Endothelial',
'Dividing cell','Lymphocyte','NK cell','Th17',
'B cell','Monocyte','Macrophages','Neutrophil',
'Dendritic cell'])
adata.obs["Celltype"] = adata.obs["Celltype"].astype('category')
adata.obs["Celltype"].cat.categories
Index(['Microglial', 'Astrocyte', 'OPC', 'ODC', 'Neuron', 'Ependymal',
'Fibroblast', 'Endothelial', 'Dividing cell', 'Lymphocyte', 'NK cell',
'Th17', 'B cell', 'Monocyte', 'Macrophages', 'Neutrophil',
'Dendritic cell'],
dtype='object')
new_colors = np.empty(len(adata.obs["Celltype"].cat.categories), dtype=object)
new_colors[[0]] = '#FF0033' # Microglial
new_colors[[1]] = '#339933' # Astrocyte
new_colors[[2]] = '#FF6600' # 'OPC'
new_colors[[3]] = '#FF9999' # Oligodendrocyte
new_colors[[4]] = '#990066' # Neuron
new_colors[[5]] = '#CC00CC' #Ependymal
new_colors[[6]] = '#33FF33' # Fibroblast
new_colors[[7]] = '#000099' # Endothelial
new_colors[[8]] = '#FFFF00' # Dividing cell
new_colors[[9]] = '#FF6666' # Lymphocyte
new_colors[[10]] ='#FFCC66' # NK cell
new_colors[[11]] ='#990000' # Th17
new_colors[[12]] ='#FF9933' # B cell
new_colors[[13]] ='#0099CC' # Monocyte
new_colors[[14]] ='#0066CC' # Macrophages
new_colors[[15]] ='#9999FF' # Neutrophil
new_colors[[16]] ='#3399FF' # Dendritic cell
adata.uns["Celltype_colors"] = new_colors
new_colors
array(['#FF0033', '#339933', '#FF6600', '#FF9999', '#990066', '#CC00CC',
'#33FF33', '#000099', '#FFFF00', '#FF6666', '#FFCC66', '#990000',
'#FF9933', '#0099CC', '#0066CC', '#9999FF', '#3399FF'],
dtype=object)
sc.settings.figdir = './result/'
sc.settings.set_figure_params(dpi=100,dpi_save=600,figsize=(8,8))
sc.pl.umap(adata, color='Celltype',legend_loc='on data',size=5,
legend_fontsize=8, legend_fontoutline= 2,title='Cell Type',add_outline=True,
frameon= True,save='celltype.pdf')
WARNING: saving figure to file result\umapcelltype.pdf
%load_ext rpy2.ipython
The rpy2.ipython extension is already loaded. To reload it, use: %reload_ext rpy2.ipython
%%R
library(future)
library(RColorBrewer)
library(viridis)
library(Seurat)
library(harmony)
library(tidyverse)
library(patchwork)
library(ggplot2)
library(cowplot)
library(dplyr)
library(data.table)
adata
AnnData object with n_obs × n_vars = 88076 × 25375
obs: 'ID', 'Type', 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'n_counts', 'log_counts', 'doublet_scores', 'predicted_doublets', 'doublet_info', 'cluster_0.1', 'cluster_0.2', 'cluster_0.3', 'cluster_0.4', 'cluster_0.5', 'cluster_0.6', 'cluster_0.7', 'cluster_0.8', 'cluster_0.9', 'cluster_1.0', 'cluster_1.1', 'cluster_1.2', 'cluster_1.3', 'cluster_1.4', 'cluster_1.5', 'Cell type', 'Celltype'
var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'Type_colors', 'doublet_info_colors', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'tsne', 'leiden', 'cluster_0.2_colors', 'cluster_0.3_colors', 'cluster_0.4_colors', 'rank_genes_groups', 'cluster_0.1_colors', 'cluster_0.5_colors', 'cluster_0.6_colors', 'cluster_0.7_colors', 'cluster_0.8_colors', 'cluster_0.9_colors', 'cluster_1.0_colors', 'cluster_1.1_colors', 'cluster_1.2_colors', 'cluster_1.3_colors', 'cluster_1.4_colors', 'cluster_1.5_colors', 'Cell type_colors', 'Celltype_colors'
obsm: 'X_pca', 'X_umap', 'X_tsne'
varm: 'PCs'
obsp: 'distances', 'connectivities'
adata.obs["Type"] = adata.obs.Type.cat.set_categories([
'Control','Vehicle', 'Drug', 'Taxol'])
adata.obs["Type"] = adata.obs["Type"].astype('category')
adata.obs["Type"].cat.categories
Index(['Control', 'Vehicle', 'Drug', 'Taxol'], dtype='object')
Sample = adata.obs['Type']
cell_type = adata.obs['Celltype']
A = adata.obs
%%R -i A -o rt
rt <- A
match_celltype_levels <- c('Microglial', 'Astrocyte', 'OPC', 'ODC', 'Neuron',
'Ependymal', 'Fibroblast', 'Endothelial', 'Dividing cell', 'Lymphocyte',
'NK cell', 'Th17', 'B cell', 'Monocyte', 'Macrophages', 'Neutrophil',
'Dendritic cell')
allcolour <- c('#FF0033', '#339933', '#FF6600', '#FF9999', '#990066', '#CC00CC',
'#33FF33', '#000099', '#FFFF00', '#FF6666', '#FFCC66', '#990000',
'#FF9933', '#0099CC', '#0066CC', '#9999FF', '#3399FF')
a <- rt
length(colnames(a))
colnames(a)[34] = c('cell.type')
a[1:10,]
mydata <- a%>%
group_by(Type) %>%
mutate(celltype = factor(cell.type,levels = match_celltype_levels)) %>%
arrange(celltype)
p1 = ggplot() +
geom_bar(data = mydata, aes(x = Type, fill = factor(celltype)), position = position_fill(reverse = TRUE)) +scale_fill_manual(values = allcolour) +
labs(fill = "cell type",x ='Group', y = "Fraction of cells")+theme(
panel.grid.major = element_blank(), #主网格线
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = 'white'), #背景色
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid")
)
p1
ggsave('./result/Ratios_barplot.pdf',p1,width = 8,height = 12,dpi = 1000)
cluster = {
'Microglial cell':["Apoe",'Ctss'],
"Astrocyte":["Aqp4","Gfap"],
"OPC":["Olig2"],
"ODC":["Plp1"],
'Neuron':["Map2","Dcx"],
'Ependymal':["Tmem212","Foxj1"],
'Fibroblast':["Dcn","Lum"],
'Vascular endothelial cell':["Pecam1"],
'Macrophage':["Ms4a7","Cd74"],
'Lymphocyte':["Cd3d"],
'Endothelial':["Cldn5","Pecam1"],
'Dividing cell':["Cdk1","Mki67"],
'Lymphocyte':["Cd3d"],
'NK cell':["Nkg7"],
'Th17':["Rora"],
'B cell':["Cd79a","Cd79b"],
'Monocyte':["Ly6c2","Csf1r"],
'Macrophages':[ "Lyz2","Cd68"],
'Neutrophils':["S100a8","S100a9"],
'Dendritic cell':['Ccr7',"Cd74"]
}
marker_list = []
for k,v in cluster.items():
if isinstance(v,list):
marker_list.extend(v)
else:
marker_list.append(v)
sc.pl.dotplot(adata, cluster, 'Celltype', dendrogram=True,cmap='OrRd',save= 'DOTmarker1.pdf')
sc.pl.dotplot(adata,marker_list, 'Celltype',cmap='OrRd',swap_axes=True,save= 'DOTmarker2.pdf')
WARNING: dendrogram data not found (using key=dendrogram_Celltype). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
using 'X_pca' with n_pcs = 30
Storing dendrogram info using `.uns['dendrogram_Celltype']`
WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different.
categories: Microglial, Astrocyte, OPC, etc.
var_group_labels: Microglial cell, Astrocyte, OPC, etc.
WARNING: saving figure to file result\dotplot_DOTmarker1.pdf
WARNING: saving figure to file result\dotplot_DOTmarker2.pdf
sc.pl.stacked_violin(adata, cluster, 'Celltype', cmap='OrRd',swap_axes=False,save='marker.pdf')
WARNING: saving figure to file result\stacked_violin_marker.pdf
import gc
gc.collect()
145687
adata.write('./01.single data/scRNA_celltype.h5ad')
sc.settings.figdir = './result/'
sc.settings.set_figure_params(dpi=100,dpi_save=600,figsize=(5,5))
sc.pl.umap(adata,color=marker_list,ncols=4,cmap='OrRd',size=15,legend_fontsize=12,save='allmarker.pdf')
WARNING: saving figure to file result\umapallmarker.pdf
--------------------------------------------------------------------------- MemoryError Traceback (most recent call last) Input In [382], in <cell line: 3>() 1 sc.settings.figdir = './result/' 2 sc.settings.set_figure_params(dpi=100,dpi_save=600,figsize=(5,5)) ----> 3 sc.pl.umap(adata,color=marker_list,ncols=4,cmap='OrRd',size=15,legend_fontsize=12,save='allmarker.pdf') File D:\conda\lib\site-packages\scanpy\plotting\_tools\scatterplots.py:669, in umap(adata, **kwargs) 610 @_wraps_plot_scatter 611 @_doc_params( 612 adata_color_etc=doc_adata_color_etc, (...) 616 ) 617 def umap(adata, **kwargs) -> Union[Axes, List[Axes], None]: 618 """\ 619 Scatter plot in UMAP basis. 620 (...) 667 tl.umap 668 """ --> 669 return embedding(adata, 'umap', **kwargs) File D:\conda\lib\site-packages\scanpy\plotting\_tools\scatterplots.py:459, in embedding(adata, basis, color, gene_symbols, use_raw, sort_order, edges, edges_width, edges_color, neighbors_key, arrows, arrows_kwds, groups, components, dimensions, layer, projection, scale_factor, color_map, cmap, palette, na_color, na_in_legend, size, frameon, legend_fontsize, legend_fontweight, legend_loc, legend_fontoutline, colorbar_loc, vmax, vmin, vcenter, norm, add_outline, outline_width, outline_color, ncols, hspace, wspace, title, show, save, ax, return_fig, **kwargs) 457 return fig 458 axs = axs if grid else ax --> 459 _utils.savefig_or_show(basis, show=show, save=save) 460 if show is False: 461 return axs File D:\conda\lib\site-packages\scanpy\plotting\_utils.py:312, in savefig_or_show(writekey, show, dpi, ext, save) 310 show = settings.autoshow if show is None else show 311 if save: --> 312 savefig(writekey, dpi=dpi, ext=ext) 313 if show: 314 pl.show() File D:\conda\lib\site-packages\scanpy\plotting\_utils.py:288, in savefig(writekey, dpi, ext) 286 # output the following msg at warning level; it's really important for the user 287 logg.warning(f'saving figure to file {filename}') --> 288 pl.savefig(filename, dpi=dpi, bbox_inches='tight') File D:\conda\lib\site-packages\matplotlib\pyplot.py:958, in savefig(*args, **kwargs) 955 @_copy_docstring_and_deprecators(Figure.savefig) 956 def savefig(*args, **kwargs): 957 fig = gcf() --> 958 res = fig.savefig(*args, **kwargs) 959 fig.canvas.draw_idle() # need this if 'transparent=True' to reset colors 960 return res File D:\conda\lib\site-packages\matplotlib\figure.py:3019, in Figure.savefig(self, fname, transparent, **kwargs) 3015 for ax in self.axes: 3016 stack.enter_context( 3017 ax.patch._cm_set(facecolor='none', edgecolor='none')) -> 3019 self.canvas.print_figure(fname, **kwargs) File D:\conda\lib\site-packages\matplotlib\backend_bases.py:2319, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs) 2315 try: 2316 # _get_renderer may change the figure dpi (as vector formats 2317 # force the figure dpi to 72), so we need to set it again here. 2318 with cbook._setattr_cm(self.figure, dpi=dpi): -> 2319 result = print_method( 2320 filename, 2321 facecolor=facecolor, 2322 edgecolor=edgecolor, 2323 orientation=orientation, 2324 bbox_inches_restore=_bbox_inches_restore, 2325 **kwargs) 2326 finally: 2327 if bbox_inches and restore_bbox: File D:\conda\lib\site-packages\matplotlib\backend_bases.py:1648, in _check_savefig_extra_args.<locals>.wrapper(*args, **kwargs) 1640 _api.warn_deprecated( 1641 '3.3', name=name, removal='3.6', 1642 message='%(name)s() got unexpected keyword argument "' 1643 + arg + '" which is no longer supported as of ' 1644 '%(since)s and will become an error ' 1645 '%(removal)s') 1646 kwargs.pop(arg) -> 1648 return func(*args, **kwargs) File D:\conda\lib\site-packages\matplotlib\_api\deprecation.py:386, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs) 381 @functools.wraps(func) 382 def wrapper(*inner_args, **inner_kwargs): 383 if len(inner_args) <= name_idx and name not in inner_kwargs: 384 # Early return in the simple, non-deprecated case (much faster than 385 # calling bind()). --> 386 return func(*inner_args, **inner_kwargs) 387 arguments = signature.bind(*inner_args, **inner_kwargs).arguments 388 if is_varargs and arguments.get(name): File D:\conda\lib\site-packages\matplotlib\backends\backend_pdf.py:2785, in FigureCanvasPdf.print_pdf(self, filename, dpi, bbox_inches_restore, metadata) 2780 file.newPage(width, height) 2781 renderer = MixedModeRenderer( 2782 self.figure, width, height, dpi, 2783 RendererPdf(file, dpi, height, width), 2784 bbox_inches_restore=bbox_inches_restore) -> 2785 self.figure.draw(renderer) 2786 renderer.finalize() 2787 if not isinstance(filename, PdfPages): File D:\conda\lib\site-packages\matplotlib\artist.py:73, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs) 71 @wraps(draw) 72 def draw_wrapper(artist, renderer, *args, **kwargs): ---> 73 result = draw(artist, renderer, *args, **kwargs) 74 if renderer._rasterizing: 75 renderer.stop_rasterizing() File D:\conda\lib\site-packages\matplotlib\artist.py:50, in allow_rasterization.<locals>.draw_wrapper(artist, renderer) 47 if artist.get_agg_filter() is not None: 48 renderer.start_filter() ---> 50 return draw(artist, renderer) 51 finally: 52 if artist.get_agg_filter() is not None: File D:\conda\lib\site-packages\matplotlib\figure.py:2810, in Figure.draw(self, renderer) 2807 # ValueError can occur when resizing a window. 2809 self.patch.draw(renderer) -> 2810 mimage._draw_list_compositing_images( 2811 renderer, self, artists, self.suppressComposite) 2813 for sfig in self.subfigs: 2814 sfig.draw(renderer) File D:\conda\lib\site-packages\matplotlib\image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 130 if not_composite or not has_images: 131 for a in artists: --> 132 a.draw(renderer) 133 else: 134 # Composite any adjacent images together 135 image_group = [] File D:\conda\lib\site-packages\matplotlib\artist.py:50, in allow_rasterization.<locals>.draw_wrapper(artist, renderer) 47 if artist.get_agg_filter() is not None: 48 renderer.start_filter() ---> 50 return draw(artist, renderer) 51 finally: 52 if artist.get_agg_filter() is not None: File D:\conda\lib\site-packages\matplotlib\axes\_base.py:3082, in _AxesBase.draw(self, renderer) 3079 a.draw(renderer) 3080 renderer.stop_rasterizing() -> 3082 mimage._draw_list_compositing_images( 3083 renderer, self, artists, self.figure.suppressComposite) 3085 renderer.close_group('axes') 3086 self.stale = False File D:\conda\lib\site-packages\matplotlib\image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 130 if not_composite or not has_images: 131 for a in artists: --> 132 a.draw(renderer) 133 else: 134 # Composite any adjacent images together 135 image_group = [] File D:\conda\lib\site-packages\matplotlib\artist.py:37, in allow_rasterization.<locals>.draw_wrapper(artist, renderer) 35 if artist.get_rasterized(): 36 if renderer._raster_depth == 0 and not renderer._rasterizing: ---> 37 renderer.start_rasterizing() 38 renderer._rasterizing = True 39 renderer._raster_depth += 1 File D:\conda\lib\site-packages\matplotlib\backends\backend_mixed.py:83, in MixedModeRenderer.start_rasterizing(self) 79 r = process_figure_for_rasterizing(self.figure, 80 self._bbox_inches_restore) 81 self._bbox_inches_restore = r ---> 83 self._raster_renderer = self._raster_renderer_class( 84 self._width*self.dpi, self._height*self.dpi, self.dpi) 85 self._renderer = self._raster_renderer File D:\conda\lib\site-packages\matplotlib\backends\backend_agg.py:93, in RendererAgg.__init__(self, width, height, dpi) 91 self.width = width 92 self.height = height ---> 93 self._renderer = _RendererAgg(int(width), int(height), dpi) 94 self._filter_renderers = [] 96 self._update_methods() MemoryError: In RendererAgg: Out of memory
